


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  argparse.ArgumentParser(
       description=(
           '''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
           ''' Distillation'''
       )
   )
   parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
   parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
   parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
   parser.add_argument('''--vocab_transform''', action='''store_true''')
   SCREAMING_SNAKE_CASE						:Dict			=  parser.parse_args()

   if args.model_type == "roberta":
      SCREAMING_SNAKE_CASE						:Union[str, Any]			=  RobertaForMaskedLM.from_pretrained(args.model_name)
      SCREAMING_SNAKE_CASE						:Dict			=  '''roberta'''
   elif args.model_type == "gpt2":
      SCREAMING_SNAKE_CASE						:Optional[Any]			=  GPTaLMHeadModel.from_pretrained(args.model_name)
      SCREAMING_SNAKE_CASE						:int			=  '''transformer'''

   SCREAMING_SNAKE_CASE						:Tuple			=  model.state_dict()
   SCREAMING_SNAKE_CASE						:Optional[int]			=  {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         SCREAMING_SNAKE_CASE						:List[Any]			=  state_dict[F'''{prefix}.{param_name}''']
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         SCREAMING_SNAKE_CASE						:str			=  F'''{prefix}.embeddings.{w}.weight'''
         SCREAMING_SNAKE_CASE						:int			=  state_dict[param_name]
      for w in ["weight", "bias"]:
         SCREAMING_SNAKE_CASE						:str			=  F'''{prefix}.embeddings.LayerNorm.{w}'''
         SCREAMING_SNAKE_CASE						:Dict			=  state_dict[param_name]

    # Transformer Blocks #
   SCREAMING_SNAKE_CASE						:List[str]			=  0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               SCREAMING_SNAKE_CASE						:Any			=  state_dict[
                   F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
               ]
         SCREAMING_SNAKE_CASE						:Union[str, Any]			=  state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               SCREAMING_SNAKE_CASE						:Dict			=  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         SCREAMING_SNAKE_CASE						:List[Any]			=  state_dict[F'''{layer}''']
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            SCREAMING_SNAKE_CASE						:Union[str, Any]			=  state_dict[F'''lm_head.dense.{w}''']
            SCREAMING_SNAKE_CASE						:Optional[Any]			=  state_dict[F'''lm_head.layer_norm.{w}''']
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         SCREAMING_SNAKE_CASE						:str			=  state_dict[F'''{prefix}.ln_f.{w}''']
      SCREAMING_SNAKE_CASE						:str			=  state_dict['''lm_head.weight''']

   print(F'''N layers selected for distillation: {std_idx}''')
   print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

   print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
   torch.save(compressed_sd, args.dump_checkpoint)








"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_							=							{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['MobileBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileBertForMaskedLM',
		    'MobileBertForMultipleChoice',
		    'MobileBertForNextSentencePrediction',
		    'MobileBertForPreTraining',
		    'MobileBertForQuestionAnswering',
		    'MobileBertForSequenceClassification',
		    'MobileBertForTokenClassification',
		    'MobileBertLayer',
		    'MobileBertModel',
		    'MobileBertPreTrainedModel',
		    'load_tf_weights_in_mobilebert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileBertForMaskedLM',
		    'TFMobileBertForMultipleChoice',
		    'TFMobileBertForNextSentencePrediction',
		    'TFMobileBertForPreTraining',
		    'TFMobileBertForQuestionAnswering',
		    'TFMobileBertForSequenceClassification',
		    'TFMobileBertForTokenClassification',
		    'TFMobileBertMainLayer',
		    'TFMobileBertModel',
		    'TFMobileBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mobilebert import (
		    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileBertConfig,
		    MobileBertOnnxConfig,
		)
		from .tokenization_mobilebert import MobileBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mobilebert_fast import MobileBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilebert import (
				    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertLayer,
				    MobileBertModel,
				    MobileBertPreTrainedModel,
				    load_tf_weights_in_mobilebert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilebert import (
				    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertMainLayer,
				    TFMobileBertModel,
				    TFMobileBertPreTrainedModel,
				)

else:
		import sys

		lowercase_							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)